The study of the structure – anticancer activity relationship of 4-thiazolidinone derivatives using multivariate adaptive regression splines

Authors

  • O. T. Devinyak Lviv National Medical University named after Danylo Halytsky, Ukraine
  • R. B. Lesyk State Higher Educational Institution "Uzhgorod National University", Ukraine

DOI:

https://doi.org/10.24959/ophcj.13.757

Keywords:

4-thiazolidinones, anticancer activity, QSAR, multivariate adaptive regression splines, MARS, molecular descriptors

Abstract

Combining the bagging method with multivariate adaptive regression splines the complex QSAR-model that incorporates 200 submodels has been developed. The predictive ability of the new model has the superiority over already known 4-thiazolidinones anticancer activity models. The interpretation of the most significant descriptors used in the model reveals promising templates for the design of new anticancer agents. The earlier conclusion that small sizes of molecules together with the absence of bonds with high dipole moments increase the antitumor activity has been confirmed. The influence of 3D-MoRSE descriptors values on the capability of compounds to inhibit the cancer cells growth has been found.

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References

  1. Lesyk R.B., Zimenkovsky B.S. // Current Org. Chem. – 2004. – Vol. 16. – P. 1547-1577.
  2. Verma A., Saraf S.K. // Eur. J. of Medicinal Chem. – 2008. – Vol. 5. – P. 897-905.
  3. Lesyk R., Zimenkovsky B., Kaminskyy D. et al. // Biopolymers and Cell. – 2011. – Vol. 2. – P. 107-117.
  4. Jain A., Vaidya A., Ravichandran V. et al. // Bioorg. & Med. Chem. – 2012. – Vol. 11. – P. 3378.
  5. Lesyk R., Zimenkovsky B., Atamanyuk D. et al. // Bioorg. & Med. Chem. – 2006. – Vol. 15. – P. 5230-40.
  6. Subtel’na I., Atamanyuk D., Szymanska E. et al. // Bioorg. & Med. Chem. – 2010. – Vol. 14. – P. 5090-102.
  7. Kaminskyy D., Khyluk D., Vasylenko O. et al. // Sci. Pharm. – 2011. – Vol. 4. – P. 763-77.
  8. Havrylyuk D., Zimenkovsky B., Vasylenko O. et al. // J. of Med. Chem. – 2012. – Vol. 20. – P. 8630-41.
  9. Kryshchyshyn A., Atamanyuk D., Lesyk R. // Sci. Pharm. – 2012. – Vol. 3. – P. 509-29.
  10. Зіменковський Б.С., Девіняк О.Т., Гаврилюк Д.Я., Лесик Р.Б. // ЖОФХ. – 2011. – №3. – P. 64-71.
  11. Зіменковський Б.С., Девіняк О.Т., Лесик Р.Б. // ЖОФХ. – 2012. – №4. – P. 76-82.
  12. Крищишин А.П., Драпак І.В., Зіменковський Б.С. та ін. // Клінічна фармація, фармакотерапія та медична стандартизація. – 2011. – №1-2. – P. 190-198.
  13. Мирко І.І., Огурцов В.В., Камінський Д.В. // Фармац. журн. – 2010. – №4. – P. 50-54.
  14. Огурцов В.В., Зіменковський Б.С., Олійник І.І. та ін. // Фармац. журн. – 2010. – №4. – P. 50-54.
  15. Девіняк О.Т., Гаврилюк Д.Я., Зіменковський Б.С., Лесик Р.Б. // Клінічна фармація, фармакотерапія та медична стандартизація. – 2011. – №3-4. – P. 163-168.
  16. Зіменковський Б.С., Девіняк О.Т., Лесик Р.Б. // ЖОФХ. – 2012. – №2. – P. 43-49.
  17. Devinyak O., Zimekovsky B., Lesyk R. // Current Topics in Med. Chem. – 2012. – Vol. 24. – P. 2763-2784.
  18. Breiman L. // Machine Learning. – 2001. – Vol. 1. – P. 5-32.
  19. Nguyen-Cong V., Van Dang G., Rode B.M. // Eur. J. of Med. Chem. – 1996. – Vol. 10. – P. 797-803.
  20. Jalali-Heravi M., Mani-Varnosfaderani A. // QSAR & Combinatorial Sci. – 2009. – Vol. 9. – P. 946-958.
  21. Alamdari R.F., Mani-Varnosfaderani A., Asadollahi-Baboli M., Khalafi-Nezhad A. // SAR and QSAR in Environmental Res. – 2012. – Vol. 7-8. – P. 665-682.
  22. Deconinck E., Xu Q.S., Put R. et al. // J. of Pharmac. and Biomed. Analysis. – 2005. – Vol. 5. – P. 1021-1030.
  23. Xu Q.S., Daszykowski M., Walczak B. et al. // Chemometrics and Intelligent Laboratory Systems. – 2004. – Vol. 1. – P. 27-34.
  24. Friedman J.H. // The Annals of Statistics. – 1991. – P. 1-67.
  25. Boyd M.R., Paull K.D. // Drug Development Res. – 1995. – Vol. 2. – P. 91-109.
  26. Shoemaker R.H. // Nature Reviews Cancer. – 2006. – Vol. 10. – P. 813-823.
  27. Buuren van S., Groothuis-Oudshoorn K. // J. of Statistical Software. – 2011. – Vol. 3. – Р. 98-101.
  28. Tetko I., Gasteiger J., Todeschini R. et al. // J. of Computer-Aided Molecular Design. – 2005. – Vol. 6. – P. 453-463.
  29. R: a language and environment for statistical computing / Core Team R. – Vienna, Austria: R Foundation for Statistical Computing, 2012.
  30. Kuhn M. // J. of Statistical Software. – 2008. – Vol. 5. – P. 1-26.
  31. Milborrow S. // R Software Package. – 2009. – Р. 38-45.
  32. Gasteiger J., Sadowski J., Schuur J. et al. // J. of Chemical Information and Computer Sci. – 1996. – Vol. 5. – P. 1030-1037.
  33. Schuur J.H., Selzer P., Gasteiger J. // J. of Chemical Information and Computer Sci. – 1996. – Vol. 2. – P. 334-344.
  34. Todeschini R., Consonni V. Handbook of Molecular Descriptors. – Wiley-VСH, 2008. – 688 p.

Published

2013-09-10

How to Cite

(1)
Devinyak, O. T.; Lesyk, R. B. The Study of the Structure – Anticancer Activity Relationship of 4-Thiazolidinone Derivatives Using Multivariate Adaptive Regression Splines. J. Org. Pharm. Chem. 2013, 11, 62-67.

Issue

Section

Original Researches